License plate recognition technology is the basis for intelligent transportation. In recent years, with the development of the digital image processing, pattern recognition and artificial intelligence technology, the license plate recognition technology is also developing continuously.
Generally, a license plate recognition process comprises the following three steps: license plate localization, character segmentation, and character recognition. Those three steps are implemented by a plurality of deep learning models. For example, during the license plate localization, the feature extraction is performed on an image to be recognized by means of a feature extraction model to obtain a feature map, and the position of a candidate box is determined by means of a region candidate localization model. A character recognition deep learning model segments characters and recognizes characters based on the feature map and the candidate box.
In the license plate recognition process described above, because the plurality of deep learning models exist independently, many redundant computations take place when operations are performed by means of the deep learning models, resulting in a large amount of redundant computational variables. For example, in the license plate localization process, a convolution operation is required to extract image features. However, the same convolution operation is usually required to extract image features during character segmentation and character recognition as well. These features become redundant computational variables. Because the large amount of redundant computational variables take up a large amount of RAM, and meanwhile there are redundant computations, the speed of license plate recognition is low.